AI Companies Are Scaling Like Services, Not Software

Title: AI companies are scaling like services, not software

Read time: 3.5 min

Today I want to discuss the idea of building AI products that indirectly rely on services behind the scenes to actually deliver results.

From the conversations we’ve had, the companies we’ve worked with, and what we’re seeing across the market, we get deep insights into different patterns unfolding. A number of AI companies that are positioned as software businesses are operating much closer to services businesses in practice.

The distinction is not semantic. It fundamentally changes how the company grows, how it prices, and what it takes to deliver value to a customer.

What’s interesting is that this shift doesn’t happen because founders set out to build services companies. It emerges as a byproduct of trying to make powerful but imperfect technology work in real world environments. The product looks clean in a demo, but once it is deployed inside an org, variability shows up quickly. Workflows are inconsistent, data is messy, and the conditions required for the product to perform as expected are rarely present out of the box.

At that point, the gap between what the product promises and what the customer needs has to be closed somehow. In many cases, that responsibility falls back on the team. They step in to adjust outputs, refine how the system is being used, and shape the implementation so that the result aligns with the expectation set during the sales process.

None of this is inherently problematic. In fact, it is often necessary early on. The issue is that over time, this pattern becomes embedded in how the product is delivered. What was initially a temporary bridge becomes part of the operating model.

A Lot Of People Build Something And Then Try To Figure Out How To Sell It. That Almost Never Works.

~Sam Altman

What’s Happening Underneath The Hood

The current AI cycle has made it ridiculously easy to build something that appears complete. Products can demonstrate real capability in a controlled environment, and that capability translates well in early conversations with buyers. The challenge begins once that capability is introduced into the complexity of an actual business.

What we’ve consistently observed is that success depends less on the product functioning in isolation and more on how well it is integrated into an existing workflow. That integration is rarely seamless. It requires interpretation, adjustment, and in many cases, active involvement from the team behind the product.

This is where the model of the business begins to shift. Instead of the product being the sole driver of value, value is delivered through a combination of product and human input. The customer still experiences a solution, but the mechanics behind that solution are different from what a traditional software model would imply.

Over time, this creates predictable constraints in my experience. Each new customer introduces a slightly different set of requirements, and implementation carries its own nuances. Progress sometimes is made, but it is not always linear, and it often depends on continued effort from the company delivering the product.

Why This Matters More Than It Seems

The reason this matters is not because services are inherently bad. Many great businesses are built on services. The issue arises when there is a mismatch between how the business is described and how it actually operates.

When a company believes it is building software but is effectively running a services model, decisions around hiring, pricing, and growth are made on the wrong assumptions. Margins behave differently, and timelines typically stretch. Scaling requires more coordination than expected.

This disconnect tends to surface gradually. It shows up in longer onboarding cycles, heavier customer involvement, and the need to stay close to each account to ensure outcomes are delivered consistently. None of these are immediate failures, but they compound over time, and can definitely neg you out.

What I’ve seen at the core of this pattern is a simple reality. AI systems are pretty sick, but they are not yet universally reliable across all contexts without some level of adaptation. Real-world environments introduce variables that are difficult to fully anticipate during product development.

As a result, teams compensate. They intervene where the system falls short, refine how it is used, and ensure that the customer ultimately sees the value that was promised. This approach works, but it introduces a dependency that is easy to underestimate.

The longer that dependency exists without being addressed, the more it defines the business, and jams up predictable sales.

Why? Because what often follows is a form of unintentional complexity. The product continues to evolve, but it does so alongside a layer of human-driven processes that are not always visible externally. The company grows, but the underlying system becomes harder to standardize.

At that point, the question is no longer just about improving the product. It becomes a question of whether the product can eventually stand on its own without the same level of support.

What Founders Should Do Instead

So what is the workaround in my opinion. I think one is to start with clarity. Founders need a precise understanding of how value is delivered today, not how it is intended to be delivered. That means mapping the full journey from first interaction to successful outcome and identifying where human involvement is required to make that outcome possible. This is why sales and building predicatble proceseses are so important.

Once that is clear, the next step is not to eliminate those touchpoints immediately, but to prioritize them. Which parts of the process are essential because of the problem being solved, and which exist because the product is not yet complete? That distinction determines where effort should be applied.

I believe, progress comes from systematically reducing reliance on manual intervention in the areas that matter most, and that does not happen all at once. It happens through tightening the use case, narrowing the conditions under which the product is sold, and reinforcing the scenarios where the system performs consistently.

Equally important is discipline in what is sold. If delivering value requires significant customization, that should be treated as a signal about the product, not an opportunity to stretch it further (read last weeks newsletter where I discussed this). The more consistent the conditions under which the product operates, the easier it becomes to move toward something that is truly repeatable.

Key Takeaway

The takeaway here is not that services are a problem. It is that understanding the nature of your business is critical to building it correctly.

If a product depends on ongoing involvement to deliver results, that is worth recognizing early. It provides clarity on where the product stands today and what needs to change to reach the level of scalability most founders are aiming for.

At Rampd, this is a conversation we have often with founders. Not to point out flaws, but to help them see the system as it actually operates, tighten the core use case, and move toward something that works predictably without constant intervention.

Once that shift happens, the business begins to behave very differently. What are you waiting for?

That’s it for today, folks.

See you all next week!

Darren


P.S. If finding PMF and scaling to $1M in ARR through founder-led sales is on your radar, book a call with me here

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